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Online learning has become a ubiquitous part of the educational landscape and how teachers are supported in developing approaches to teaching online is a fundamental aspect of the students' learning experience. Based on the implementation of a professional development course on becoming an online teacher offered in a blended learning mode at one university in Hong Kong, this article proposes that offering this type of professional development in a blended mode is very effective in facilitating enhanced usage of the university's learning management system. In a blended mode, teachers are actively engaged with blended learning and were found to make more extensive use of features/ tools in Blackboard after they attended the professional development course. Results support that offering professional development in a blended mode provides teachers with an authentic student perspective, at the same time as they take guided steps in the teacher's role in blended learning.

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region. Copyright Springer 2006Annual Traffic Census, Auto-Regressive Integrated Moving Average, Gaussian Maximum Likelihood, Neural Network, Non-Parametric Regression,

This paper investigates the use of real-time automatic vehicle identification (AVI) data and an offline travel time database for real-time estimation of arterial travel times in Hong Kong, China. The offline database consists of average link travel times and spatial link travel time covariance matrices by time of day, day of week, and week of month. Three-month historical travel time estimates and real-time AVI data are adopted for calibration and updating of the spatial covariance relationships of link travel times on Hong Kong arterial roads. A case study has been carried out on a selected path in a Hong Kong urban area to evaluate the performance of three alternative methods for real-time estimation of arterial travel times: fixed offline database (Method 1), continuously updated offline database (Method 2), and continuously updated offline database generated by the nonparametric regression method (Method 3). The validation results show that the travel time estimation errors of Methods 2 and 3 are significantly reduced when compared with those using the fixed offline database.

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Most of the existing crack growth models rely on empirical constants derived from curve fits of data at specific test conditions. Although statistical information can be obtained for many of these constants, multiple experimental tests typically must be performed to represent the wide range of the response. In this paper, an alternative approach is presented that links fatigue crack growth parameters to material and microstructural size parameters via a microstructure-based fatigue crack growth (FCG) model. In addition, variation of initial crack size due to microstructural variation is modeled in terms of a crack-size-based fatigue crack initiation model. Variations of microstructural parameters are described in terms of a probabilistic framework. The probabilistic, microstructure-based, FCG approach is illustrated for a Ni-based superalloy in which the influence of changes in the main descriptors of the individual microstructural parameters on initial crack size, crack growth rate, and fatigue life is shown. Stochastic model results are compared with existing experimental data to illustrate the feasibility of the approach for predicting da/dN variability due to microstructure variations.

Traditionally, students learned rat dissection through reading printed dissection guides, observing teacher demonstration, and hands-on dissection practice in a Biology laboratory. Teacher demonstration was always not very effective for a large class of students. Hands-on practice consumed a lot of rats before students could master the basic skills of rat dissection. Expenditure in terms of time and resources was high in the learning of rat dissection. To help students learn rat dissection in a more cost-effective manner, the authors designed and developed an interactive multimedia courseware. In addition, the authors had made an innovative use of multimedia technology to perform assessment in a manner which was not possible with traditional media. This article summarized the findings of an evaluation study of the interactive multimedia courseware. The evaluation study looked into students' attitude toward the interactive multimedia courseware, and the problems encountered during the use of the courseware. Lastly, the authors' reflection of the development process and product courseware also constituted part of the evaluation.